Propensity Model Using Decision Trees (LightGBM) for the Management of the Effective Credit Product in a Financial Entity
The objective of this paper was to develop a propensity model based on decision trees (LightGbm) for the management of the Credit product in a financial institution. The CRIPS-DM methodology was used as a framework and Python/LightGBM was used for the development of the solution. As a result, it was possible to increase by 5% the effectiveness in credit for each month on a park of 200 thousand customers of the financial institution, which ensures the understanding of the applied model.
Authors
- Carlos Chávez-Herrera
- Norberto Ulises Roman Concha (ORCID: https://orcid.org/0000-0002-3302-7539)
- José Piedra
- Andrea López (ORCID: https://orcid.org/0000-0002-7612-2595)
- Carlos Navarro (ORCID: https://orcid.org/0000-0002-6697-8365)
- Kathy Ruiz-Carrasco
- Dominga M. Cano
- Juan Carlos Woolcott
Publication Details
- Journal
- Advances in Artificial Intelligence and Machine Learning
- Published
- 2025-01-01
- DOI
- https://doi.org/10.54364/aaiml.2025.51184
- Citations
- 1
- Primary Topic
- Financial Distress and Bankruptcy Prediction
- Type
- article
- Field-Weighted Citation Impact
- 3.91